arXiv subjects

Martin de los Rios

Publications and source records attributed to Martin de los Rios.

3 recordsLinked to original sources

arXiv2025

Bayesian technique to combine independently-trained Machine-Learning models applied to direct dark matter detection

We carry out a Bayesian analysis of dark matter (DM) direct detection data to determine particle model parameters using the Truncated Marginal Neural Ratio Estimation (TMNRE) machine learning technique. TMNRE avoids an explicit calculation of the likelihood, which instead is estimated from simulated data, unlike in traditional Markov Chain Monte Carlo (MCMC) algorithms. This considerably speeds up, by several orders of magnitude, the computation of the posterior distributions, which allows to perform the Bayesian analysis of an otherwise computationally prohibitive number of benchmark points. In this article we demonstrate that, in the TMNRE framework, it is possible to include, combine, and remove different datasets in a modular fashion, which is fast and simple as there is no need to re-train the machine learning algorithm or to define a combined likelihood. In order to assess the performance of this method, we consider the case of WIMP DM with spin-dependent and independent interactions with protons and neutrons in a xenon experiment. After validating our results with MCMC, we employ the TMNRE procedure to determine the regions where the DM parameters can be reconstructed. Finally, we present CADDENA, a Python package that implements the modular Bayesian analysis of direct detection experiments described in this work.

hep-ph↗

arXiv2023

Machine-Learned Exclusion Limits without Binning

Machine-Learned Likelihoods (MLL) combines machine-learning classification techniques with likelihood-based inference tests to estimate the experimental sensitivity of high-dimensional data sets. We extend the MLL method by including Kernel Density Estimators (KDE) to avoid binning the classifier output to extract the resulting one-dimensional signal and background probability density functions. We first test our method on toy models generated with multivariate Gaussian distributions, where the true probability distribution functions are known. Later, we apply the method to two cases of interest at the LHC: a search for exotic Higgs bosons, and a $Z'$ boson decaying into lepton pairs. In contrast to physical-based quantities, the typical fluctuations of the ML outputs give non-smooth probability distributions for pure-signal and pure-background samples. The non-smoothness is propagated into the density estimation due to the good performance and flexibility of the KDE method. We study its impact on the final significance computation, and we compare the results using the average of several independent ML output realizations, which allows us to obtain smoother distributions. We conclude that the significance estimation turns out to be not sensible to this issue.

hep-ph↗

arXiv2022

Reconstructing Orbits of Galaxies in Extreme Regions (ROGER) III: galaxy evolution patterns in projected phase space around massive X-ray clusters

We use the ROGER code by de los Rios et al. to classify galaxies around a sample of X-ray clusters into five classes according to their positions in the projected phase space diagram: cluster galaxies, backsplash galaxies, recent infallers, infalling galaxies, and interlopers. To understand the effects of the cluster environment to the evolution of galaxies, we compare across the five classes: stellar mass, specific star formation rate, size, and morphology. Following the guidelines of Coenda et al., a separate analysis is carried out for red and blue galaxies. For red galaxies, cluster galaxies differ from the other classes, having a suppressed specific star formation rate, smaller sizes, and are more likely to be classified as ellipticals. Differences are smaller between the other classes, however backsplash galaxies have significantly lower specific star formation rates than early or recent infalling galaxies. For blue galaxies, we find evidence that recent infallers are smaller than infalling galaxies and interlopers, while the latter two are comparable in size. Our results provide evidence that, after a single passage, the cluster environment can diminish a galaxy's star formation, modify its morphology, and can also reduce in size blue galaxies. We find evidence that quenching occurs faster than morphological transformation from spirals to ellipticals for all classes. While quenching is evidently enhanced as soon as galaxies get into clusters, significant morphological transformations require galaxies to experience the action of the physical mechanisms of the cluster for longer timescales.

astro-ph.GA↗